Overview

Air traffic management (ATM) can greatly benefit from cyber-secured exploitation of large private data sets belonging to different stakeholders. Currently, however, there is a reluctance to share sensitive data. AICHAIN is an European research project funded by SESAR JU under EU´s H2020 framework that is investigating the technologies of privacy preserving federated machine learning (PPFML) and blockchain to enable collaboration among ATM actors and stakeholders in building shared data-driven machine learning models with data privacy protection by design, with the end goal of enabling ATM operational improvements. The concept is being experimented with ATM use cases selected in the area of demand capacity balancing of the network, with pre-existing predictive models being augmented with real operational private data from airspace users. Many cases of federated learning collaborations can be identified between Airlines, Airport Operators, Air Navigation Service Providers and Air Traffic Control, and Network Manager, fostering opportunities of improvement of machine learning and artificial intelligence applications in the ATM domain. This project has received funding from the SESAR Joint Undertaking (JU) under grant agreement No 894162. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union. The opinions expressed herein reflect the project author’s view only. Under no circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information contained herein.